Defect detection using structural information

ABSTRACT

Systems and methods for detecting defects on a specimen based on structural information are provided. One system includes one or more computer subsystems configured for separating the output generated by a detector of an inspection subsystem in an array area on a specimen into at least first and second segments of the output based on characteristic(s) of structure(s) in the array area such that the output in different segments has been generated in different locations in the array area in which the structure(s) having different values of the characteristic(s) are formed. The computer subsystem(s) are also configured for detecting defects on the specimen by applying one or more defect detection methods to the output based on whether the output is in the first segment or the second segment.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to systems and methods fordefect detection using structural information.

2. Description of the Related Art

The following description and examples are not admitted to be prior artby virtue of their inclusion in this section.

Inspection processes are used at various steps during a semiconductormanufacturing process to detect defects on wafers to promote higheryield in the manufacturing process and thus higher profits. Inspectionhas always been an important part of fabricating semiconductor devicessuch as ICs. However, as the dimensions of semiconductor devicesdecrease, inspection becomes even more important to the successfulmanufacture of acceptable semiconductor devices because smaller defectscan cause the devices to fail.

Some current inspection methods for array areas on specimens includeperforming defect detection in a rectangular care area covering anentire array region. Gray level values in images generated for the arrayareas are used to divide the images into different areas (calledsegmentation). These areas are meant to correspond to differentstructures of the specimen. The gray level segmentation may then be usedto guide sensitivity settings for defect detection.

However, correspondence between gray level values and wafer structuresmay not be unique. For example, n-type metal-oxide-semiconductor (NMOS)and p-type MOS (PMOS) structures may have similar gray level values. Dueto process variation, the gray level values on the same structure canvary across specimens or between specimens. Consequently, the result maynot be a clear indication about wafer structures.

Accordingly, it would be advantageous to develop systems and methods fordetecting defects on a specimen that do not have one or more of thedisadvantages described above.

SUMMARY OF THE INVENTION

The following description of various embodiments is not to be construedin any way as limiting the subject matter of the appended claims.

One embodiment relates to a system configured to detect defects on aspecimen based on structural information. The system includes aninspection subsystem that includes at least an energy source and adetector. The energy source is configured to generate energy that isdirected to a specimen. The detector is configured to detect energy fromthe specimen and to generate output responsive to the detected energy.The system also includes one or more computer subsystems configured forseparating the output generated by the detector in an array area on thespecimen into at least first and second segments of the output based onone or more characteristics of one or more structures in the array areasuch that output in different segments has been generated in differentlocations in the array area in which the one or more structures havingdifferent values of the one or more characteristics are formed. Inaddition, the one or more computer subsystems are configured fordetecting defects on the specimen by applying one or more defectdetection methods to the output based on whether the output is in thefirst segment or the second segment.

Another embodiment relates to a computer-implemented method fordetecting defects on a specimen based on structural information. Themethod includes the separating and detecting steps described above. Theseparating and detecting steps are performed by one or more computersubsystems.

Each of the steps of the method may be further performed as describedherein. In addition, the method may include any other step(s) of anyother method(s) described herein. Furthermore, the method may beperformed by any of the systems described herein.

Another embodiment relates to a non-transitory computer-readable mediumstoring program instructions executable on a computer system forperforming a computer-implemented method for detecting defects on aspecimen based on structural information. The computer-implementedmethod includes the steps of the method described above. Thecomputer-readable medium may be further configured as described herein.The steps of the computer-implemented method may be performed asdescribed further herein. In addition, the computer-implemented methodfor which the program instructions are executable may include any otherstep(s) of any other method(s) described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Further advantages of the present invention will become apparent tothose skilled in the art with the benefit of the following detaileddescription of the preferred embodiments and upon reference to theaccompanying drawings in which:

FIG. 1 is a schematic diagram illustrating a side view of an embodimentof a system configured to detect defects on a specimen based onstructural information;

FIG. 2 is an image of an example of an array area on a specimen and animage of a cell within the array area;

FIG. 3 is an image of an example of a portion of an array area on aspecimen with structural information correlated thereto;

FIG. 4 is a gray level representation of an example of a portion of anarray area on a specimen;

FIG. 5 is a schematic diagram illustrating an embodiment of a spatialrelationship of the portion of the array area shown in FIG. 4;

FIG. 6 is a schematic diagram illustrating an embodiment of a binarymask representation of the portion of the array area shown in FIG. 4;

FIGS. 7-9 are different images of an example of a portion of an arrayarea on a specimen;

FIG. 10 is an image of an example of a portion of an array area on aspecimen with different structures defined therein;

FIG. 11 is a schematic diagram of an example of a unique structure thatmay be formed on a specimen and a spatial relationship between theunique structure and one or more structures in an array area of thespecimen;

FIG. 12 is an image of the unique structure and one or more structuresshown in FIG. 11;

FIG. 13 is a schematic diagram of an example of unique structures thatmay be formed on a specimen and used for locating one or more structuresin an array area on the specimen;

FIGS. 14-16 are plots showing examples of different results of applyingone or more defect detection methods to output of a detector of aninspection subsystem; and

FIG. 17 is a block diagram illustrating one embodiment of anon-transitory computer-readable medium storing program instructions forcausing a computer system to perform a computer-implemented methoddescribed herein.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and are herein described in detail. The drawingsmay not be to scale. It should be understood, however, that the drawingsand detailed description thereto are not intended to limit the inventionto the particular form disclosed, but on the contrary, the intention isto cover all modifications, equivalents and alternatives falling withinthe spirit and scope of the present invention as defined by the appendedclaims.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Turning now to the drawings, it is noted that the figures are not drawnto scale. In particular, the scale of some of the elements of thefigures is greatly exaggerated to emphasize characteristics of theelements. It is also noted that the figures are not drawn to the samescale. Elements shown in more than one figure that may be similarlyconfigured have been indicated using the same reference numerals. Unlessotherwise noted herein, any of the elements described and shown mayinclude any suitable commercially available elements.

The embodiments described herein generally provide new approaches todefect detection in array areas on specimens such as patternedsemiconductor wafers that include acquiring and representing structuralinformation and applying the structural information in specimeninspection. The embodiments described herein can be used to produce moreyield relevant results for such specimens. One embodiment relates to asystem configured to detect defects on a specimen based on structuralinformation. The specimen may include, in some embodiments, a wafer. Inother embodiments, the specimen may include a reticle. The wafer and thereticle may include any wafer and reticle known in the art.

The system includes an inspection subsystem that includes at least anenergy source and a detector. The energy source is configured togenerate energy that is directed to a specimen. The detector isconfigured to detect energy from the specimen and to generate outputresponsive to the detected energy. One embodiment of such an inspectionsubsystem is shown in FIG. 1 as inspection subsystem 104 of system 106.In the embodiment shown in this figure, the inspection subsystem is anoptical or light-based inspection subsystem. For example, as shown inFIG. 1, the inspection subsystem includes light source 108, which mayinclude any suitable light source known in the art such as a broadbandplasma light source.

Light from the light source may be directed to beam splitter 110, whichmay be configured to direct the light from the light source to specimen112. The light source may be coupled to any other suitable elements (notshown) such as one or more condensing lenses, collimating lenses, relaylenses, objective lenses, apertures, spectral filters, polarizingcomponents and the like. As shown in FIG. 1, the light may be directedto the specimen at a normal angle of incidence. However, the light maybe directed to the specimen at any suitable angle of incidence includingnear normal and oblique incidence. In addition, the light or multiplelight beams may be directed to the specimen at more than one angle ofincidence sequentially or simultaneously. The inspection subsystem maybe configured to scan the light over the specimen in any suitablemanner.

Light from specimen 112 may be collected and detected by one or moredetectors of the inspection subsystem during scanning. For example,light reflected from specimen 112 at angles relatively close to normal(i.e., specularly reflected light when the incidence is normal) may passthrough beam splitter 110 to lens 114. Lens 114 may include a refractiveoptical element as shown in FIG. 1. In addition, lens 114 may includeone or more refractive optical elements and/or one or more reflectiveoptical elements. Light collected by lens 114 may be focused to detector116. Detector 116 may include any suitable detector known in the artsuch as a charge coupled device (CCD) or another type of imagingdetector. Detector 116 is configured to generate output that isresponsive to the reflected light collected by lens 114. Therefore, lens114 and detector 116 form one channel of the inspection subsystem. Thischannel of the inspection subsystem may include any other suitableoptical components (not shown) known in the art. The output of thedetector may include, for example, images, image data, signals, imagesignals, or any other output that can be generated by a detectorsuitable for use in an inspection system.

Since the inspection subsystem shown in FIG. 1 is configured to detectlight specularly reflected from the specimen, the inspection subsystemis configured as a bright field (BF) inspection system. Such aninspection subsystem may, however, also be configured for other types ofinspection. For example, the inspection subsystem shown in FIG. 1 mayalso include one or more other channels (not shown). The otherchannel(s) may include any of the optical components described hereinsuch as a lens and a detector, configured as a scattered light channel.The lens and the detector may be further configured as described herein.In this manner, the inspection subsystem may also be configured for darkfield (DF) inspection.

The system also includes one or more computer subsystems. For example,as shown in FIG. 1, the system may include computer subsystem 118 thatis coupled to the inspection subsystem such that the computer subsystemcan receive output generated by the detector of the inspectionsubsystem. For example, the computer subsystem may be coupled todetector 116 and any other detectors included in the inspectionsubsystem such that the computer subsystem can receive output generatedby the detector(s). The computer subsystem is configured for performingthe steps described further herein. The computer subsystem(s) (alsoreferred to herein as “computer system(s)”) and the system may befurther configured as described herein.

It is noted that FIG. 1 is provided herein to generally illustrate aconfiguration of an inspection subsystem that may be included in thesystem embodiments described herein. Obviously, the inspection subsystemconfiguration described herein may be altered to optimize theperformance of the inspection subsystem as is normally performed whendesigning a commercial inspection system. In addition, the systemsdescribed herein may be implemented using an existing inspectionsubsystem (e.g., by adding functionality described herein to an existinginspection system) such as the 29xx/28xx series of tools that arecommercially available from KLA-Tencor, Milpitas, Calif. For some suchsystems, the methods described herein may be provided as optionalfunctionality of the system (e.g., in addition to other functionality ofthe system). Alternatively, the system described herein may be designed“from scratch” to provide a completely new system.

Furthermore, although the system is described herein as being an opticalor light-based inspection system, the inspection subsystem may beconfigured as an electron beam based inspection subsystem (not shown).For example, the energy source of the inspection subsystem may beconfigured to generate electrons and the detector may be configured todetect electrons returned from the specimen. The electron beam basedinspection subsystem may be any suitable electron beam based inspectionsubsystem included in any suitable commercially available electron beaminspection system.

The computer subsystem(s) are configured for separating the outputgenerated by the detector of the inspection subsystem in an array areaon the specimen into at least first and second segments of the outputbased on one or more characteristics of one or more structures in thearray area such that the output in different segments has been generatedin different locations in the array area in which the one or morestructures having different values of the one or more characteristicsare formed. The term “array area” refers to the area in a die where thepattern repeats periodically. The basic repeating pattern is called a“cell.” Array areas are usually inspected by a cell-to-cell comparisoninstead of a die-to-die comparison. FIG. 2 illustrates one example of anarray area and a cell contained therein. For example, as shown in FIG.2, image 200 of an array area shows the repeating structures that may beformed within the array area. The basic structure within the array areais called a cell, one image for which is shown by image 202, whichrepeats spatially in the array area.

The embodiments described herein use structural information for defectdetection. Structural information is more relevant and more robust forseparating defects of interest (DOIs) from nuisance events. As will bedescribed further herein, the embodiments may be configured to acquirestructural information, represent structural information, and applystructural information in specimen inspection.

Since the structural information described herein is based on thestructure(s) in an area on a specimen, the structural information mayvary from specimen type to specimen type. In other words, differentstructural information may be used to represent different devices. Theembodiments described herein may be programmed with such structureinformation. For example, during setup of inspection of the specimen,structural information for the specimen may be encoded in software to beused during the inspection. Separating the output may be performedduring inspection of the specimen as the output is acquired. In thismanner, the structures on the specimen may be identified duringinspection (i.e., during run time).

In one embodiment, separating the output is not based on one or morecharacteristics of the output. For example, some currently usedinspection methods perform segmentation of inspection system outputbased on gray levels of the output. In contrast, separating the outputas described herein may be performed independent of any characteristicsof the output itself. For example, even if the output generated fordifferent instances of one type of structure formed on a specimen, whichhas one or more structural characteristics that are different from othertypes of structures on the specimen, has different gray levels, all ofthat output may be separated into the same segment. In this manner, theoutput separated into one of the segments may have differentcharacteristics of the output even though it was all generated fordifferent instances of the same structure formed on the specimen.

Unlike the embodiments described herein, therefore, current segmentationmay be based on gray levels which may not indicate structures on thespecimen. Therefore, segmentation based on gray levels is different thanthe segmentation for the array areas described herein which is based onstructure information. Structures indicate functionality and process ofwafer patterns and noise sources. Inspection guided by structures willproduce more yield relevant results.

In one such example, in currently performed die-to-die (random area) orcell-to-cell (array area) inspection, output generated for a region inthe area of the specimen being inspected may be separated into differentsegments based on gray levels or other characteristics of the output.However, in the embodiments described herein for cell-to-cell (array)inspection, output generated for a region in the area of the specimenbeing inspected may be separated, as described further herein, intofirst output generated at locations of a first type of structure on thespecimen, second output generated at locations of a second type ofstructure on the specimen, and so on. All of the output determined to begenerated for one type of the structures may then be further segmentedbased on any other information (e.g., gray levels) available to theembodiments described herein. Similar segmentation may be separatelyperformed for any of the other structure types identified in theinspection subsystem output.

In another embodiment, the one or more characteristics of the structuresare determined based on one or more scanning electron microscope (SEM)images of the specimen or another specimen of the same type as thespecimen. In this manner, the structural information used in theembodiments described herein may be acquired through SEM image(s). Assuch, the embodiments described herein may be configured forunderstanding structures such as wafer structures with SEM images. Waferstructures indicate the spatial layout of polysilicon, contact holes,n-type metal-oxide-semiconductor (NMOS), p-type MOS (PMOS), etc. Userscan delineate particular structures in high resolution SEM images.Analyzing the SEM image to determine the one or more characteristics ofthe structures may be performed during setup of the inspection performedfor the specimen. In this manner, the embodiments described herein donot necessarily use or need information in a design file.

In an additional embodiment, separating the output includes correlatingone or more SEM images of the specimen or another specimen of the sametype as the specimen with the output and transferring the one or morecharacteristics of the one or more structures from the one or more SEMimages to the output correlated thereto. For example, by correlating SEMimages with inspection images, structural information can be transferredfrom SEM images onto inspection images. In one such example, as shown inFIG. 3, inspection image 300 of a portion of an array area may begenerated by an inspection subsystem as described herein. Structuralinformation 302, which may be acquired from a SEM image, may then bealigned with the inspection image. The structural information can thenbe assigned to the pixels in the inspection image to which it has beenaligned. In this manner, if structural information 302 containsinformation for different structures, different structural informationmay be assigned to the pixels in the portion of the inspection image towhich the structural information has been aligned. Correlating the SEMimage with the inspection image and representing the structuralinformation in the inspection image may be performed during setup of theinspection performed for the specimen. In addition, the same structurerepeats in the array areas. In order to find the same structure inimages during inspection only the basic structure, for example, a patchimage of array at a particular die location may be stored. The patchimage represents the structural information.

In a further embodiment, the one or more characteristics of thestructures are determined based on a design for the specimen. In thismanner, the structural information used in the embodiments describedherein may be acquired through a design file. For example, theembodiments described herein may be configured to understand thestructures on the specimen with semiconductor design files such asgraphical data stream (GDS) files. Similar to the SEM images, users cancorrelate design patterns to optical images. Analyzing the design todetermine the one or more characteristics of the structures may beperformed during setup of the inspection performed for the specimen. Thedesign used in the embodiments described herein does not include anyrendered, synthetic, or simulated images from the design.

In some embodiments, separating the output includes correlating a designfor the specimen with the output and transferring the one or morecharacteristics of the one or more structures from the design to theoutput correlated thereto. For example, by correlating the design withinspection images, structural information can be transferred from thedesign onto inspection images. Such correlating may be performed asdescribed further herein with respect to SEM images. For example,structural information 302 shown in FIG. 3 may be from a design patternfor the specimen. The structural information from the design can then beassigned to the pixels in the inspection image to which the design hasbeen aligned. In this manner, the structural information can be carriedthrough inspection images and geometry obtained from semiconductordesign. Correlating the design with the inspection image andrepresenting the structural information in the inspection image may beperformed during setup of the inspection performed for the specimen. Inaddition, the same structure repeats in the array areas. In order tofind the same structure in images during inspection only the basicstructure, for example, a patch image of array at a particular dielocation may be stored. The patch image represents the structuralinformation.

In a further embodiment, separating the output includes applying one ormore rules to the output, and the one or more rules are based ondifferences in the one or more characteristics between different typesof the one or more structures. For example, the structural informationused in the embodiments described herein may be acquired through userknowledge. In this manner, the embodiments described herein may be usedto delineate wafer structural information based on user knowledge. Inaddition, the inspection image can be compared to a SEM image asdescribed further herein for the same structure, then rules can becreated to identify the structure in the inspection image. In thismanner, the embodiments described herein may analyze patterns insidearray areas. Multiple rules may be applied based on structuralinformation input from users.

During set up of the embodiments described herein using an image-basedapproach, a set of rules to identify array structures based on commonpatterns of structures may be defined and encoded in software. A usermay input structure information based on the image of array structures.The computer subsystem(s) may then analyze the image intensities,projection, variance, and symmetry and determine which rules should beapplied to identify array structures for this type of array areas on thewafer. These steps may be performed for one type of array structure andthen repeated for any other types of array structures. The computersubsystem(s) may then save the rules into an inspection recipe. Duringdefect detection, given an image, the computer subsystem(s) may applythe predetermined rules to the output of the inspection subsystem. Sincethe array structures repeat, an averaged image of multiple repeats canbe calculated to reduce the noise on the image. The structures may thenbe identified in the output.

The structural information may be represented in a number of differentways. For example, the structural information can be transferred andrepresented in terms of gray levels or spatial relationship of graylevels or binary masks. Examples of such representations are shown inFIGS. 4-6. For example, as shown in FIG. 4, gray level representation400 may be used to represent structural information. In addition, asshown in FIG. 5, spatial relationship 500 may be used to represent thesame structural information as shown in FIG. 4. For example, darkportions 502 of FIG. 5 may correspond to the structures shown in FIG. 4having a “zigzag” or asymmetrical shape while white portions 504 maycorrespond to the structures shown in FIG. 4 that have a symmetricalshape. Furthermore, as shown in FIG. 6, binary mask 600 may be used torepresent the same structural information as shown in FIG. 4.

In this manner, identifying the different portions of the outputgenerated in different portions of the specimen containing differentstructures during run time may be performed in a number of differentways. For example, if the structural information is identified in thedesign file, the design patterns can be aligned to the patterns in theinspection image, then structures in the inspection image, whichcorrespond to the structural information in the design patterns, can beidentified. This approach is a type of design-to-image alignment. Inaddition, the inspection image generated during setup that containsstructural information can be correlated to the inspection imagegenerated during run time. This approach is a type of image-to-imagealignment. Rules of structural information can also be programmed insoftware. At run time, the software may identify the structuralinformation based on rides. This approach is a kind of image analysis.

In one embodiment, the one or more computer subsystems are configuredfor locating the one or more structures in the output based on theoutput generated for one or more unique structures formed on thespecimen and a spatial relationship between the unique structures andthe one or more structures. The embodiments described herein thereforeprovide a specific approach to pixel-to-design alignment. For example,unique structures such as corners or page breaks can be used to locatenon-unique (repeating) structures by defining spatial offsets. Inparticular, since patterns in array (e.g., SRAM) areas are dense andrepeating, the patterns are not unique in this area and are not good foralignment. The non-repeating patterns close to memory areas are neararray corners. In order to perform alignment near array corners, thealignment sites may be searched first. The embodiments described hereinare therefore unlike currently used methods in which images are analyzedsince this approach does not ensure that all sites found by search arenear the desired areas such as SRAM corners. Instead, the embodimentsdescribed herein may use the design information. In this manner, SRAMcorners and other unique structures in the design can be easilyidentified in design files. The computer subsystem(s) may be configuredto find the unique structures in a recipe setup stage. The locations ofthese unique structures can be refined during inspection. The locationscan housed to identify structures in inspection images. For example,during alignment site search, the SRAM corners generated from designfiles may be provided to the computer subsystem(s).

In this manner, the unique structure(s) can be used to help align a maskof a specimen structure to an image. For example, as shown in FIG. 11,during set up of the inspection process, unique structure 1100 outsideof the array structures may be identified and registered to mask 1102 ofthe structures in the array area. In other words, a spatial relationshipbetween the unique structure and the mask may be determined duringsetup. In this manner, as shown in FIG. 12, during inspection, image1200 of the unique structure may be aligned with the unique structureimage acquired during set up and the mask of the array structure shouldbe aligned with array structure 1202 in image 1204. FIG. 13 illustratesadditional examples of unique structures that may be used by theembodiments described herein. For example, the unique structures myinclude SRAM corners 1300 of SRAM blocks 1302.

In some embodiments, the one or more characteristics include a repeatingpitch of the one or more structures. For example, the repeating pitch ofarray structures can be part of the information used to representstructures (i.e., in combination with other structural informationdescribed herein such as symmetry information). In general, however, therepeating pitch of the structures would not be used by itself in theembodiments described herein.

In another embodiment, the one or more characteristics include one ormore topologic characteristics of the one or more structures. In thismanner, the structural information may include topologic information.Topologic information is advantageous for use in the embodimentsdescribed herein since such information describes properties ofgeometric forms that remain invariant under certain transformations suchas bending or stretching. Therefore, the structural information used inthe embodiments described herein my remain the same even if the shape ordimension of the structure varies from one device to another device.

In some embodiments, the one or more characteristics include symmetricaland asymmetrical characteristics of the one or more structures.Symmetrical information about the structures formed on the specimen isone example of structural information that is more robust to colorvariation. For example, as shown in FIG. 7, an inspection image for aspecimen may include horizontally asymmetrical intensities 700 thatcorrespond to a first structure and symmetrical intensities 702 thatcorrespond to a second structure different than the first. If image graylevel is used to describe the first and second structures using thisinspection image, then the bright intensities correspond to the firststructure and the dark intensities correspond to the second structure.This description of the relationship between intensities and structuresis suitable for the inspection image shown in FIG. 7, but not for theinspection image shown in FIG. 9, which is generated for the same areaas that included in the inspection image of FIG. 7. In particular, inthe inspection image of FIG. 9, dark intensities 900 are horizontallyasymmetrical and correspond to the first structure while brightintensities 902 are symmetrical and correspond to the second structure.For the image shown in FIG. 8, the shape and gray levels of asymmetricalstructures 800 and symmetrical structures 802 are different from theimages shown in FIGS. 7 and 9. However, symmetry information is stillpreserved in this image. As such, symmetry is a better, robustdescriptor no matter how gray level changes. For example, if opticsmode(s) of the inspection subsystem are changed, the gray levels ofstructures may change dramatically while the symmetry characteristics ofthe structures and their corresponding intensities in the images remainunchanged. In this manner, the embodiments described herein may beconfigured to use symmetry information from both images and design toidentify different structures in inspection subsystem output.

In another embodiment, separating the output includes determiningsymmetry scores for different regions in the output and a design for thewafer, aligning the symmetry scores for the output to the symmetryscores for the design, and separating the output based on results of thealigning and information about which of the one or more structurescorrespond to the symmetry scores for the design. In this manner, theembodiments described herein may be configured for symmetry-basedalignment. For example, a key enabler for context based inspection (CBI)may be pixel-to-design alignment (PDA). In one such example, a CBIsystem may render an image of a design clip and align it with acorresponding wafer image. With a relatively good PDA, the care areagenerated from design can be aligned to the wafer image substantiallyaccurately.

However, design clips and wafer images have substantially differentmodalities. The interactions among the wafer printing process, waferpattern, and inspection system is extremely complicated. These problemsmake image rendering the most difficult task in PDA. Although astatistical algorithm may be used to mitigate the impact from theunsatisfactory image rendering to a relatively large extent and providea considerable cushion to the individual PDA misalignment, the need forindividual PDA at the subpixel level for design dips that are difficultto render, such as the areas near SRAM areas, is inevitable.

Symmetry-based alignment is inspired by the fact that symmetry is afundamental characteristic of geometry in many design clips andinspection images. In addition, symmetry is often more invariant tolarge appearance changes than image intensity or gradients.

Symmetry-based alignment may include calculation of the symmetry scorefor each pixel and the alignment of the symmetry score. For example, thecomputer subsystem(s) may be configured to determine first symmetryscores from the pattern in a design and second symmetry scores from thepattern in an inspection image. The calculation of the symmetry may beperformed to determine how similar an image region, when flipped aboutan axis, is to another image region. The computer subsystem(s) may thencompute offsets by correlation of the first and second symmetry scores.The design and inspection image may then be aligned by applying theoffsets. The alignment of the symmetry scores is to find the bestsubpixel offsets that can maximize the cross-correlation between thesymmetry scores of the design clips and that of the inspection images.

In one embodiment, the first and second segments are located in one ormore care areas. For example, the embodiments described herein do notchange the care areas selected for array inspection, which defineregions for cell-to-cell inspection. Instead, masks may be used insidearray care areas and indicate different detection sensitivities. Forexample, for die-to-die inspection, since a pixel is compared to anotherpixel in another die, die-to-die inspection does not require specifyingneighboring information, for example, an area for cell-to-cellinspection. However, for cell-to-cell inspection in array regions, apixel is compared to another pixel in the same image. Region isspecifically used to indicate the areas for cell-to-cell inspection.Therefore, the embodiments described herein do not change the region orcare area in which inspection is performed in the array area. Thesegmentation described herein is performed within the region or carearea in which the inspection is performed.

The computer subsystem(s) are also configured for detecting defects onthe specimen by applying one or more defect detection methods to theoutput based on whether the output is in the first segment or the secondsegment. In this manner, the embodiments described herein perform defectdetection using structural information. In addition, the one or moredefect detection methods may be applied separately and independently tothe different segments of the output. For example, the defect detectionperformed in one of the segments may be different from the defectdetection performed in another of the different segments. In addition,defect detection may not be performed for all of the different segments.For instance, depending on the structures whose output is included ineach of the different segments, defect detection may be performed in oneof the different segments but not another of the different segments.

In an additional embodiment, applying the defect detection method(s)includes applying the one or more defect detection methods having afirst set of parameters to only the output included in the first segmentand applying the one or more defect detection methods having a secondset of parameters different from the first set of parameters to only theoutput included in the second segment. For example, different noisestatistics (depending on the defect detection method(s), e.g., a 2Dhistogram of difference vs. median gray level for the MDAT defectdetection algorithm used by some inspection systems currently availablefrom KLA-Tencor) may be generated for each different structure. Incontrast, in currently used methods, noise statistics may be calculatedon a region basis, i.e., a multi-structure basis. Therefore, unlike thecurrently used methods, the embodiments described herein allow defectdetection sensitivity to adapt to noise for different structuresseparately. For each structure, segmentation can be used for furtherdividing images into meaningful areas. If the number of nuisance defectsis relatively large on certain structures or segments, the user can seta lower sensitivity for those structures or segments to suppressnuisance defects.

The first and second sets of parameters may include one or moredifferent parameters or values of a parameter of the same defectdetection method(s). For example, the first set of parameters for theone or more defect detection methods may include a first threshold forone defect detection algorithm and the second set of parameters for theone or more defect detection methods may include a second threshold forthe same defect detection algorithm. In a different example, the firstset of parameters may include parameters for a first defect detectionalgorithm, and the second set of parameters may include parameters for asecond defect detection algorithm different than the first. The firstand second sets of parameters may be different in any other way thatwould produce different defect detection in different segments of theoutput. In addition, the embodiments described herein are not specificto any one defect detection method or type of defect detection. Instead,the embodiments described herein can be used with any defect detectionmethod(s) whose parameters can be changed depending on the segment ofthe output to which it/they are being applied.

In another embodiment, the one or more defect detection methods appliedto the output in the first segment have a different detectionsensitivity than the one or more defect detection methods applied to theoutput in the second segment. For example, the system can use thestructural information to apply different defect detection sensitivitiesand/or algorithms to different areas that correspond to differentstructures. In this manner, different detection sensitivities and/oralgorithms may be applied to different structures based on their levelsof interest.

In one embodiment, applying the defect detection method(s) includesdetermining one or more characteristics of noise for the first segmentbased on only the output in the first segment and determining one ormore parameters of the one or more defect detection methods based on thedetermined one or more characteristics of the noise. In one suchexample, for areas where noise is relatively high, lower sensitivitiesmay be applied. On the other hand, where noise is relatively low, highersensitivities may be applied. The sensitivity used for detecting defectsin different structures having one or more different characteristics maybe determined during setup of the inspection for the specimen.

Unlike currently used methods in which noise statistics are generatedfrom different gray levels, in the embodiments described herein,different noise statistics may be generated for different structures.For example, in currently used methods, noise statistics may begenerated for the entire inspection image shown in FIG. 10. However, inthe embodiments described herein, different noise statistics may bedetermined for different structures shown in the inspection image. Forexample, first noise statistics may be determined using only portions1000 of the image and second noise statistics may be determined usingonly portions 1002 of the image. In this manner, the first noisestatistics may be determined for the portions that include mixed graylevels while the second noise statistics may be determined for theportions that include the mostly black gray levels. In another example,once the structures are identified, noise statistics such as a 2Dhistogram, which typically includes difference value vs. median graylevel, may be created for each structure. Defect detection parametersmay then be applied based on noise on each structure. In this manner,unlike methods that do not separate output based on structuralinformation for a specimen such that the same defect detectionparameters are applied to all structures, with the embodiments describedherein, different defect detection parameters can be used for differentstructures. Therefore, overall, the embodiments described herein willhave improved defect detection sensitivity.

In this manner, applying the defect detection method(s) as describedherein may be an image-based approach that enables differentsensitivities to be applied to the pixels in array areas based on theirlocations in different structures, e.g., P cell structure or N cellstructure, through image analysis. If different noise levels exist ondifferent structures, this approach will effectively suppress thenuisance events from non-structures of interest (non-SOIs) and willenhance the inspection sensitivity on SOI.

The defect detection method(s) that are applied to the output by theembodiments described herein are therefore different than currently useddefect detection methods. For example, in some currently used methods,pixel statistics may be created from both SOI and non-SOI. Since noisecan be relatively high on non-SOI, defective pixels may not bedetectable. For example, as shown in FIG. 14, a currently used defectdetection method may generate pixel statistics such as pixel count vs.gray level difference and plot 1400 of these pixel statistics may begenerated. Detection threshold 1402 may be determined based on the pixelstatistics shown in this plot. Due to noise in the pixels (e.g., non-SOIpixels), the detection threshold determined based on these statisticsmay be too high to detect some defective pixels 1404. Therefore, somedefective pixels may not be detected by the defect detection method.

In contrast, the embodiments described herein may apply a defectdetection method that generates pixel statistics from only non-SOIpixels, where the noise may be relatively high. The defect detectionmethod may generate plot 1500 shown in FIG. 15 from these statistics.Based on these statistics, detection threshold 1502 may be determined.The detection threshold determined for these non-SOI pixels may berelatively high (i.e., relatively low sensitivity) to avoid detectingthe relatively noisy pixels as defects. In addition, the embodimentsdescribed herein may apply a defect detection method that generatespixel statistics from only SOI pixels where a defect may be located andwhere noise is relatively low. The defect detection method may generateplot 1600 shown in FIG. 16 from these statistics. Based on thesestatistics, detection threshold 1602 may be determined. The detectionthreshold determined for these SOI pixels may be relatively low (i.e.,relatively high sensitivity) such that defective pixels 1604 can bedetected. In this manner, the defect detection method(s) may detect thedefective pixels as defects since the defective pixels are detectablewith the lower threshold.

In some embodiments, the one or more computer subsystems are configuredfor determining the values of the one or more characteristics of the oneor more structures in which the defects are detected and storing thedetermined values as defect attributes for the defects. For example,structural information may be calculated for each defect as a defectattribute. In this manner, structure information can be assigned todefects detected on the specimen. Assigning the structure information tothe defects may be performed during run time. These attributes can beused in defect post-processing for defect classification, sorting, ornuisance event removal. Nuisance filtering and/or defect classificationmay be performed during run time.

The embodiments described herein have a number of advantages overcurrently used methods for inspecting specimens. For example, since thestructural information can be obtained either from SEM images or designfiles, the structural information has physical meaning and is more yieldrelevant than gray level information. In addition, for differentdevices, structural information may be different. Its representation canbe customized during recipe setup. Furthermore, the structuralinformation contains topologic information which is more robust thansimple gray level values to color variation. The efficiency of nuisancesuppression can therefore be much higher. In one such example, if noisecomes from symmetrical structures and the goal is to suppress thisnoise, since the embodiments described herein can identify symmetricalstructures on the specimen, noise from these areas can be suppressed. Inanother such example, if the DOIs are located in only one particulartype of structure (e.g., structures that happen to be symmetrical), thenthe information about which type of structure a defect is located in canbe used to separate defects into DOIs and nuisances. In this manner,defect detection using structural information can provide more yieldrelevant inspection results. In addition, since the embodimentsdescribed herein do not use simulated or rendered images of a design(e.g., an image of the design as it would be imaged by an inspectionsystem after being formed on a specimen), the embodiments describedherein can be used for areas on the wafer where such simulation orrendering is particularly difficult such as the areas near SRAM areas.In this manner, the embodiments described herein can performpixel-to-design alignment for array areas for the purposes of outputsegmentation as described herein without needing or using simulated orrendered images.

Another embodiment relates to a computer-implemented method fordetecting defects on a specimen based on structural information. Themethod includes the separating and detecting steps described above.These steps are performed by one or more computer systems, which may beconfigured according to any of the embodiments described herein.

Each of the embodiments of the methods described above may include anyother step(s) of any other method(s) described herein. Furthermore, eachof the embodiments of the methods described above may be performed byany of the systems described herein.

All of the methods described herein may include storing results of oneor more steps of the method embodiments in a computer-readable storagemedium. The results may include any of the results described herein andmay be stored in any manner known in the art. The storage medium mayinclude any storage medium described herein or any other suitablestorage medium known in the art. After the results have been stored, theresults can be accessed in the storage medium and used by any of themethod or system embodiments described herein, formatted for display toa user, used by another software module, method, or system, etc.

Another embodiment relates to a non-transitory computer-readable mediumstoring program instructions executable on a computer system forperforming a computer-implemented method for detecting defects on aspecimen based on structural information. One such embodiment is shownin FIG. 17. For example, as shown in FIG. 17, non-transitorycomputer-readable medium 1700 stores program instructions 1702executable on computer system 1704 for performing a computer-implementedmethod for detecting defects on a specimen based on structuralinformation. The computer-implemented method may include any step(s) ofany method(s) described herein.

Program instructions 1702 implementing methods such as those describedherein may be stored on non-transitory computer-readable medium 1700.The computer-readable medium may be a storage medium such as a magneticor optical disk, a magnetic tape, or any other suitable non-transitorycomputer-readable medium known in the art.

The program instructions may be implemented in any of various ways,including procedure-based techniques, component-based techniques, and/orobject-oriented techniques, among others. For example, the programinstructions may be implemented using Matlab, Visual Basic, ActiveXcontrols, C, C++ objects, C#, JavaBeans, Microsoft Foundation Classes(“MFC”), or other technologies or methodologies, as desired.

Computer system 1704 may take various forms, including a personalcomputer system, mainframe computer system, workstation, systemcomputer, image computer, programmable image computer, parallelprocessor, or any other device known in the art. In general, the term“computer system” may be broadly defined to encompass any device havingone or more processors, which executes instructions from a memorymedium.

Further modifications and alternative embodiments of various aspects ofthe invention will be apparent to those skilled in the art in view ofthis description. For example, systems and methods for detecting defectson a specimen based on structural information are provided. Accordingly,this description is to be construed as illustrative only and for thepurpose of teaching those skilled in the art the general manner ofcarrying out the invention. It is to be understood that the forms of theinvention shown and described herein are to be taken as the presentlypreferred embodiments. Elements and materials may be substituted forthose illustrated and described herein, parts and processes may bereversed, and certain features of the invention may be utilizedindependently, all as would be apparent to one skilled in the art afterhaving the benefit of this description of the invention. Changes may bemade in the elements described herein without departing from the spiritand scope of the invention as described in the following claims.

What is claimed is:
 1. A system configured to detect defects on aspecimen based on structural information, comprising: an inspectionsubsystem comprising at least an energy source and a detector, whereinthe energy source is configured to generate energy that is directed to aspecimen, and wherein the detector is configured to detect energy fromthe specimen and to generate output responsive to the detected energy;and one or more computer subsystems configured for: separating theoutput generated by the detector in an array area on the specimen intoat least first and second segments of the output based on one or morecharacteristics of one or more structures in the array area such thatthe output in different segments has been generated in differentlocations in the array area in which the one or more structures havingdifferent values of the one or more characteristics are formed, whereinsaid separating comprises determining symmetry scores for differentregions in the output. and a design for the wafer, aligning symmetryscores for the output to the symmetry scores for the design, andseparating the output based on results of said aligning and informationabout which of the one or more structures correspond to the symmetryscores for the design; and detecting defects on the specimen by applyingone or more defect detection methods to the output based on whether theoutput is in the first segment or the second segment.
 2. The system ofclaim 1, wherein said separating is not based on one or morecharacteristics of the output.
 3. The system of claim 1, wherein the oneor more characteristics of the structures are determined based on one ormore scanning electron microscope images of the specimen or anotherspecimen of the same type as the specimen.
 4. The system of claim 1,wherein said separating further comprises correlating one or morescanning electron microscope images of the specimen or another specimenof the same type as the specimen with the output and transferring theone or more characteristics of the one or more structures from the oneor more scanning electron microscope images to the output correlatedthereto.
 5. The system of claim 1, wherein the one or morecharacteristics of the structures are determined based on a design forthe specimen.
 6. The system of claim 1, wherein said separating furthercomprises correlating a design for the specimen with the output andtransferring the one or more characteristics of the one or morestructures from the design to the output correlated thereto.
 7. Thesystem of claim 1, wherein the one or more computer subsystems arefurther configured for locating the one or more structures in the outputbased on the output generated for one or more unique structures formedon the specimen and a spatial relationship between the one or moreunique structures and the one or more structures.
 8. The system of claim1, wherein the one or more characteristics comprise a repeating pitch ofthe one or more structures.
 9. The system of claim 1, wherein the one ormore computer subsystems are further configured for determining thevalues of the one or more characteristics of the one or more structuresin which the defects are detected and storing the determined values asdefect attributes for the defects.
 10. The system of claim 1, whereinthe one or more characteristics comprise one or more topologiccharacteristics of the one or more structures.
 11. The system of claim1, wherein the one or more characteristics comprise symmetrical andasymmetrical characteristics of the one or more structures.
 12. Thesystem of claim 1, wherein the first and second segments are located inone or more care areas.
 13. The system of claim 1, wherein saidseparating further comprises applying one or more rules to the output,and wherein the one or more rules are based on differences in the one ormore characteristics between different types of the one or morestructures.
 14. The system of claim 1, wherein said applying comprisesdetermining one or more characteristics of noise for the first segmentbased on only the output in the first segment and determining one ormore parameters of the one or more defect detection methods based on thedetermined one or more characteristics of the noise.
 15. The system ofclaim 1, wherein the one or more defect detection methods applied to theoutput in the first segment have a different detection sensitivity thanthe one or more defect detection methods applied to the output in thesecond segment.
 16. The system of claim 1, wherein said applyingcomprises applying the one or more defect detection methods having afirst set of parameters to only the output included in the first segmentand applying the one or more defect detection methods having a secondset of parameters different from the first set of parameters to only theoutput included in the second segment.
 17. A non-transitorycomputer-readable medium, storing program instructions executable on acomputer system for performing a computer-implemented method fordetecting defects on a specimen based on structural information, whereinthe computer-implemented method comprises: separating output generatedby a detector of an inspection subsystem in an array area on a specimeninto at least first and second segments of the output based on one ormore characteristics of one or more structures in the array area suchthat the output in different segments has been generated in differentlocations in the array area in which the one or more structures havingdifferent values of the one or more characteristics are formed, whereinthe inspection subsystem comprises at least an energy source and thedetector, wherein the energy source is configured to generate energythat is directed to the specimen, wherein the detector is configured todetect energy from the specimen and to generate output responsive to thedetected energy, and wherein said separating comprises determiningsymmetry scores for different regions in the output and a design for thewafer, aligning the symmetry scores for the output to the symmetryscores for the design, and separating the output based on results ofsaid aligning and information about which of the one or more structurescorrespond to the symmetry scores for the design; and detecting defectson the specimen by applying one or more defect detection methods to theoutput based on whether the output is in the first segment or the secondsegment.
 18. A computer-implemented method for detecting defects on aspecimen based on structural information, comprising: separating outputgenerated by a detector of an inspection subsystem in an array area on aspecimen into at least first and second segments of the output based onone or more characteristics of one or more structures in the array areasuch that the output in the different segments has been generated indifferent locations in the array area in which the one or morestructures having different values of the one or more characteristicsare formed, wherein the inspection subsystem comprises at least anenergy source and the detector, wherein the energy source is configuredto generate energy that is directed to the specimen, wherein thedetector is configured to detect energy from the specimen and togenerate output responsive to the detected energy, and wherein saidseparating comprises determining symmetry scores for different regionsin the output and a design for the wafer, aligning the symmetry scoresfor the output to the symmetry scores for the design, and separating theoutput based on results of said aligning and information about which ofthe one or more structures correspond to the symmetry scores for thedesign; and detecting defects on the specimen by applying one or moredefect detection methods to the output based on whether the output is inthe first segment or the second segment, wherein said separating andsaid detecting are performed by one or more computer systems.